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Articles de revues sur le sujet « Feature processing »

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Auteur : Grafiati
Publié le 10 mars 2023

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1

Franceschetti, Giorgio, Antonio Lodice et Manlio Tesauro. « From image processing to feature processing ». Signal Processing 60, no 1 (juillet 1997) : 51–63. http://dx.doi.org/10.1016/s0165-1684(97)00064-9.

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Hema, Dr A., et R. Saravanakumar. « A Survey on Feature Extraction Technique in Image Processing ». International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (30 juin 2018) : 448–51. http://dx.doi.org/10.31142/ijtsrd12937.

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Pesti, Jaan A. « Special Feature Issue : Continuous Processing ». Organic Process Research & ; Development 18, no 11 (21 novembre 2014) : 1284–85. http://dx.doi.org/10.1021/op500323a.

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Lin, Wei, Yuefei Zhu et Ruijie Cai. « Processing of Cryptographic Function Identification Based on Multi-feature Progressive Model ». Journal of Advances in Computer Networks 3, no 3 (2015) : 180–85. http://dx.doi.org/10.7763/jacn.2015.v3.163.

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Prasad, J. V. D., Babu Sallagundla et Raghuvira Pratap A. « Multi-Feature Processing Techniques with Information Mining From Remote Sensing Images ». Journal of Advanced Research in Dynamical and Control Systems 11, no 12 (20 décembre 2019) : 97–106. http://dx.doi.org/10.5373/jardcs/v11i12/20193217.

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Taylor, Steven, et David Badcock. « Processing feature density in preattentive perception ». Perception & ; Psychophysics 44, no 6 (novembre 1988) : 551–62. http://dx.doi.org/10.3758/bf03207489.

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Visconti di Oleggio Castello, Matteo, Kelsey G. Wheeler, Carlo Cipolli et M. Ida Gobbini. « Familiarity facilitates feature-based face processing ». PLOS ONE 12, no 6 (5 juin 2017) : e0178895. http://dx.doi.org/10.1371/journal.pone.0178895.

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Wayland, Susan, et John E. Taplin. « Feature-processing deficits following brain injury ». Brain and Cognition 4, no 3 (juillet 1985) : 338–55. http://dx.doi.org/10.1016/0278-2626(85)90026-0.

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Wayland, Susan, et John E. Taplin. « Feature-processing deficits following brain injury ». Brain and Cognition 4, no 3 (juillet 1985) : 356–76. http://dx.doi.org/10.1016/0278-2626(85)90027-2.

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Halloran, John W. « Editorial on colloidal processing Centennial Feature ». Journal of the American Ceramic Society 100, no 2 (février 2017) : 457. http://dx.doi.org/10.1111/jace.14764.

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Vega-Rodriguez, M. A. « Review : Feature Extraction and Image Processing ». Computer Journal 47, no 2 (1 février 2004) : 271–72. http://dx.doi.org/10.1093/comjnl/47.2.271-a.

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Hannah, Samuel. « Feature representations and analytic/nonanalytic processing. » Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale 59, no 1 (2005) : 41–46. http://dx.doi.org/10.1037/h0087459.

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Ohl, Frank W., et Henning Scheich. « Feature extraction and feature interaction ». Behavioral and Brain Sciences 21, no 2 (avril 1998) : 278. http://dx.doi.org/10.1017/s0140525x98431170.

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The idea of the orderly output constraint is compared with recent findings about the representation of vowels in the auditory cortex of an animal model for human speech sound processing (Ohl & Scheich 1997). The comparison allows a critical consideration of the idea of neuronal “feature extractors,” which is of relevance to the noninvariance problem in speech perception.
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Wu, Pengxiang, Chao Chen, Jingru Yi et Dimitris Metaxas. « Point Cloud Processing via Recurrent Set Encoding ». Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 juillet 2019) : 5441–49. http://dx.doi.org/10.1609/aaai.v33i01.33015441.

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We present a new permutation-invariant network for 3D point cloud processing. Our network is composed of a recurrent set encoder and a convolutional feature aggregator. Given an unordered point set, the encoder firstly partitions its ambient space into parallel beams. Points within each beam are then modeled as a sequence and encoded into subregional geometric features by a shared recurrent neural network (RNN). The spatial layout of the beams is regular, and this allows the beam features to be further fed into an efficient 2D convolutional neural network (CNN) for hierarchical feature aggregation. Our network is effective at spatial feature learning, and competes favorably with the state-of-the-arts (SOTAs) on a number of benchmarks. Meanwhile, it is significantly more efficient compared to the SOTAs.
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Wang, Lu, et Xiao Ning Fu. « FPGA-Based Image Processing System for Target Locating ». Applied Mechanics and Materials 226-228 (novembre 2012) : 1878–81. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.1878.

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In this paper, we proposed the design of an FPGA-based image processing system for target locating. The locating mechanism is based on the feature line segments of target’s image. The system processes the target’s image sequence, finds and matches feature points on each image, and uses the feature points to calculate the length of feature line segments for target locating. We implemented the Speeded Up Robust Features (SURF) algorithm on FPGA hardware to extract feature points. The system has a core CPU for control and part of the mathematical computation. Custom-designed logic circuit modules are used to accelerate the feature point extraction. The system’s software is designed to work with parallel and pipeline operation. The performance test shows that the system is capable of real-time processing.
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Ren, Zhigang, Guoquan Ren et Dinhai Wu. « Deep Learning Based Feature Selection Algorithm for Small Targets Based on mRMR ». Micromachines 13, no 10 (18 octobre 2022) : 1765. http://dx.doi.org/10.3390/mi13101765.

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Small target features are difficult to distinguish and identify in an environment with complex backgrounds. The identification and extraction of multi-dimensional features have been realized due to the rapid development of deep learning, but there are still redundant relationships between features, reducing feature recognition accuracy. The YOLOv5 neural network is used in this paper to achieve preliminary feature extraction, and the minimum redundancy maximum relevance algorithm is used for the 512 candidate features extracted in the fully connected layer to perform de-redundancy processing on the features with high correlation, reducing the dimension of the feature set and making small target feature recognition a reality. Simultaneously, by pre-processing the image, the feature recognition of the pre-processed image can be improved. Simultaneously, by pre-processing the image, the feature recognition of the pre-processed image can significantly improve the recognition accuracy. The experimental results demonstrate that using the minimum redundancy maximum relevance algorithm can effectively reduce the feature dimension and identify small target features.
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17

Woods, David L., et Claude Alain. « Conjoining Three Auditory Features : An Event-Related Brain Potential Study ». Journal of Cognitive Neuroscience 13, no 4 (1 mai 2001) : 492–509. http://dx.doi.org/10.1162/08989290152001916.

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The mechanisms of auditory feature processing and conjunction were examined with event-related brain potential (ERP) recording in a task in which participants responded to target tones defined by the combination of location, frequency, and duration features amid distractor tones varying randomly along all feature dimensions. Attention effects were isolated as negative difference (Nd) waves by subtracting ERPs to tones with no target features from ERPs to tones with one, two, or three target features. Nd waves were seen to all tones sharing a single feature with the target, including tones sharing only target duration. Nd waves associated with the analysis of frequency and location features began at latencies of 60 msec, whereas Nd-Duration waves began at 120 msec. Nd waves to tones with single target features continued until 400+ msec, suggesting that once begun, the analysis of tone features continued exhaustively to conclusion. Nd-Frequency and Nd-Location waves had distinct scalp distributions, consistent with generation in different auditory cortical areas. Three stages of feature processing were identified: (1) Parallel feature processing (60-140 msec): Nd waves combined linearly, such that Nd-wave amplitudes following tones with two or three target features were equal to the sum of the Nd waves elicited by tones with only one target feature. (2) Conjunction-specific (CS) processing (140-220 msec): Nd amplitudes were enhanced following tones with any pair of attended features. (3) Target-specific (TS) processing (220-300 msec): Nd amplitudes were specifically enhanced to target tones with all three features. These results are consistent with a facilitatory interactive feature analysis (FIFA) model in which feature conjunction is associated with the amplified processing of individual stimulus features. Activation of N-methyl-D-aspartate (NMDA) receptors is proposed to underlie the FIFA process.
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Wei, Zhenfeng, et Xiaohua Zhang. « Feature Extraction and Retrieval of Ecommerce Product Images Based on Image Processing ». Traitement du Signal 38, no 1 (28 février 2021) : 181–90. http://dx.doi.org/10.18280/ts.380119.

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The new retail is an industry featured by online ecommerce. One of the key techniques of the industry is the product identification based on image processing. This technique has an important business application value, because it is capable of improving the retrieval efficiency of products and the level of information supervision. To acquire high-level semantics of images and enhance the retrieval effect of products, this paper explores the feature extraction and retrieval of ecommerce product images based on image processing. The improved Fourier descriptor was innovatively into a metric learning-based product image feature extraction network, and the attention mechanism was introduced to realize accurate retrieval of product images. Firstly, the authors detailed how to acquire the product contour and the axis with minimum moment of inertia, and then extracted the shape feature of products. Next, a feature extraction network was established based on the metric learning supervision, which is capable of obtaining distinctive feature, and thus realized the extraction of distinctive and classification features of products. Finally, the authors expounded on the product image retrieval method based on cluster attention neural network. The effectiveness of our method was confirmed through experiments. The research results provide a reference for feature extraction and retrieval in other fields of image processing.
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Megreya, Ahmed M. « Feature-by-feature comparison and holistic processing in unfamiliar face matching ». PeerJ 6 (26 février 2018) : e4437. http://dx.doi.org/10.7717/peerj.4437.

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Identity comparisons of photographs of unfamiliar faces are prone to error but imperative for security settings, such as the verification of face identities at passport control. Therefore, finding techniques to improve face-matching accuracy is an important contemporary research topic. This study investigates whether matching accuracy can be enhanced by verbal instructions that address feature comparisons or holistic processing. Findings demonstrate that feature-by-feature comparison strategy had no effect on face matching. In contrast, verbal instructions focused on holistic processing made face matching faster, but they impaired accuracy. Given the recent evidence for the heredity of face perception and the previously reported small or no improvements of face-matching ability, it seems reasonable to suggest that improving unfamiliar face matching is not an easy task, but it is presumably worthwhile to explore new methods for improvement nonetheless.
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20

Bell, J., M. Forsyth, D. R. Badcock et F. A. A. Kingdom. « Global shape processing involves feature-selective and feature-agnostic coding mechanisms ». Journal of Vision 14, no 11 (19 septembre 2014) : 12. http://dx.doi.org/10.1167/14.11.12.

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R., Indra Gandhi. « Pre-processing and Feature Extraction for a Copper Plate Character Recognition System ». Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (28 février 2020) : 1071–77. http://dx.doi.org/10.5373/jardcs/v12sp3/20201353.

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22

Pugin, E. V., et A. L. Zhiznyakov. « CLASSIFICATION OF FEATURES OF IMAGE SEQUENCES ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5/W6 (18 mai 2015) : 79–81. http://dx.doi.org/10.5194/isprsarchives-xl-5-w6-79-2015.

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Processing of image sequences is a very actual trend now. This is confirmed with a vast amount of researches in that area. The possibility of an image sequence processing and pattern recognition became available because of increased computer capabilities and better photo and video cameras. The feature extraction is one of the main steps during image processing and pattern recognition. This paper presents a novel classification of features of image sequences. The proposed classification has three groups: 1) features of a single image, 2) features of an image sequence, 3) semantic features of an observed scene. The first group includes features extracted from a single image. The second group consists of features of any kinds of image sequences. The third group contains semantic features. Reverse feature clarification method is the iterative method when on each iteration we use higher level features to extract lower level features more precisely. The proposed classification of features of image sequences solves a problem of decomposition of the source feature space into several groups. Reverse feature clarification method allows to increase the quality of image processing during iterative process.
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23

Yörük, Harun, et Aysecan Boduroglu. « Feature-specificity in visual statistical summary processing ». Attention, Perception, & ; Psychophysics 82, no 2 (3 janvier 2020) : 852–64. http://dx.doi.org/10.3758/s13414-019-01942-x.

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Zhang, Weiwei, et Steven J. Luck. « Feature-based attention modulates feedforward visual processing ». Nature Neuroscience 12, no 1 (23 novembre 2008) : 24–25. http://dx.doi.org/10.1038/nn.2223.

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Fornaciai, Michele, et Joonkoo Park. « Spatiotemporal feature integration shapes approximate numerical processing ». Journal of Vision 17, no 13 (6 novembre 2017) : 6. http://dx.doi.org/10.1167/17.13.6.

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Hawkins, R., J. Houpt, A. Eidels, J. Townsend et M. Wenger. « Fundamental properties of simple emergent feature processing ». Journal of Vision 12, no 9 (10 août 2012) : 1298. http://dx.doi.org/10.1167/12.9.1298.

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Balfour, C., J. S. Smith et S. Amin-Nejad. « Feature correlation for weld image-processing applications ». International Journal of Production Research 42, no 5 (mars 2004) : 975–95. http://dx.doi.org/10.1080/00207540310001619632.

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Shi, Chun Hua. « Image processing and feature extraction of microscopic ». MATEC Web of Conferences 44 (2016) : 01086. http://dx.doi.org/10.1051/matecconf/20164401086.

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Chung, Susana T. L., et Daniel R. Coates. « Spatio-Temporal Dependencies of Letter Feature Processing ». Journal of Vision 19, no 10 (6 septembre 2019) : 65b. http://dx.doi.org/10.1167/19.10.65b.

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Greenberg, Steven, et Thomas Ulrich Christiansen. « The perceptual flow of phonetic feature processing ». Journal of the Acoustical Society of America 123, no 5 (mai 2008) : 3932. http://dx.doi.org/10.1121/1.2935993.

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Nestor, A., et M. Tarr. « Task-specific feature codes for face processing ». Journal of Vision 8, no 6 (27 mars 2010) : 530. http://dx.doi.org/10.1167/8.6.530.

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Dhawan, Atam P., et Eric Le Royer. « Mammographic feature enhancement by computerized image processing ». Computer Methods and Programs in Biomedicine 27, no 1 (juillet 1988) : 23–35. http://dx.doi.org/10.1016/0169-2607(88)90100-9.

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Zirnsak, M., et F. H. Hamker. « Attention Alters Feature Space in Motion Processing ». Journal of Neuroscience 30, no 20 (19 mai 2010) : 6882–90. http://dx.doi.org/10.1523/jneurosci.3543-09.2010.

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Liu, Z. Q., T. Austin, C. D. L. Thomas et J. G. Clement. « Bone feature analysis using image processing techniques ». Computers in Biology and Medicine 26, no 1 (janvier 1996) : 65–76. http://dx.doi.org/10.1016/0010-4825(95)00044-5.

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Glass, A. M. « Feature Issue On Materials For Optical Processing ». Journal of the Optical Society of America B 3, no 2 (1 février 1986) : 245. http://dx.doi.org/10.1364/josab.3.000245.

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Xu, Yihui, Yajun Tong, Menglu Hu, Jiadong Fan et Huaidong Jiang. « Applying Feature Detection to XPCS Image Processing ». Journal of Physics : Conference Series 2380, no 1 (1 décembre 2022) : 012124. http://dx.doi.org/10.1088/1742-6596/2380/1/012124.

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Abstract Sequential X-ray photon correlation spectroscopy (XPCS) reveals sample dynamics by analyzing a series of coherent scattering images, which is often time-consuming. For applications like real-time XPCS analysis, high efficiency is desired. Pixel binning is a straightforward strategy to reduce the processing time, but over-binning may result in an insufficient signal-to-noise ratio. In this work, feature detection is applied to obtain the optimal binning factor for the XPCS image processing. Results show that under optimal binning, the processing time is reduced by more than one order of magnitude. In addition, it is illustrated that feature detection could potentially be applied to other coherent imaging and scattering techniques such as coherent diffraction imaging (CDI).
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Zhao, Xin, Wenqian Shen et Guanjun Wang. « Early Prediction of Sepsis Based on Machine Learning Algorithm ». Computational Intelligence and Neuroscience 2021 (12 octobre 2021) : 1–13. http://dx.doi.org/10.1155/2021/6522633.

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Sepsis is an organ failure disease caused by an infection resulting in extremely high mortality. Machine learning algorithms XGBoost and LightGBM are applied to construct two processing methods: mean processing method and feature generation method, aiming to predict early sepsis 6 hours in advance. The feature generation methods are constructed by combining different features, including statistical strength features, window features, and medical features. Miceforest multiple interpolation method is applied to tackle large missing data problems. Results show that the feature generation method outperforms the mean processing method. XGBoost and LightGBM algorithms are both excellent in prediction performance (AUC: 0.910∼0.979), among which LightGBM boasts a faster running speed and is stronger in generalization ability especially on multidimensional data, with AUC reaching 0.979 in the feature generation method. PTT, WBC, and platelets are the key risk factors to predict early sepsis.
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Zhao, Xin, Wenqian Shen et Guanjun Wang. « Early Prediction of Sepsis Based on Machine Learning Algorithm ». Computational Intelligence and Neuroscience 2021 (12 octobre 2021) : 1–13. http://dx.doi.org/10.1155/2021/6522633.

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Sepsis is an organ failure disease caused by an infection resulting in extremely high mortality. Machine learning algorithms XGBoost and LightGBM are applied to construct two processing methods: mean processing method and feature generation method, aiming to predict early sepsis 6 hours in advance. The feature generation methods are constructed by combining different features, including statistical strength features, window features, and medical features. Miceforest multiple interpolation method is applied to tackle large missing data problems. Results show that the feature generation method outperforms the mean processing method. XGBoost and LightGBM algorithms are both excellent in prediction performance (AUC: 0.910∼0.979), among which LightGBM boasts a faster running speed and is stronger in generalization ability especially on multidimensional data, with AUC reaching 0.979 in the feature generation method. PTT, WBC, and platelets are the key risk factors to predict early sepsis.
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Perruchet, Pierre, et Annie Vinter. « Feature creation as a byproduct of attentional processing ». Behavioral and Brain Sciences 21, no 1 (février 1998) : 33–34. http://dx.doi.org/10.1017/s0140525x9844010x.

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Attributing the creation of new features to categorization requirements implies that the exemplars displayed are correctly assigned to their category. This constraint limits the scope of Schyns et al.'s proposal to supervised learning. We present data suggesting that this constraint is unwarranted and we argue that feature creation is better thought of as a byproduct of the attentional, on-line processing of incoming information.
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Zhang, Xiaohan, Shaonan Wang, Nan Lin, Jiajun Zhang et Chengqing Zong. « Probing Word Syntactic Representations in the Brain by a Feature Elimination Method ». Proceedings of the AAAI Conference on Artificial Intelligence 36, no 10 (28 juin 2022) : 11721–29. http://dx.doi.org/10.1609/aaai.v36i10.21427.

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Neuroimaging studies have identified multiple brain regions that are associated with semantic and syntactic processing when comprehending language. However, existing methods cannot explore the neural correlates of fine-grained word syntactic features, such as part-of-speech and dependency relations. This paper proposes an alternative framework to study how different word syntactic features are represented in the brain. To separate each syntactic feature, we propose a feature elimination method, called Mean Vector Null space Projection (MVNP). This method can remove a specific feature from word representations, resulting in one-feature-removed representations. Then we respectively associate one-feature-removed and the original word vectors with brain imaging data to explore how the brain represents the removed feature. This paper for the first time studies the cortical representations of multiple fine-grained syntactic features simultaneously and suggests some possible contributions of several brain regions to the complex division of syntactic processing. These findings indicate that the brain foundations of syntactic information processing might be broader than those suggested by classical studies.
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Brummerloh, Berit, et Matthias M. Müller. « Time matters : Feature-specific prioritization follows feature integration in visual object processing ». NeuroImage 196 (août 2019) : 81–93. http://dx.doi.org/10.1016/j.neuroimage.2019.04.023.

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Eckstein, Miguel P. « The Lower Visual Search Efficiency for Conjunctions Is Due to Noise and not Serial Attentional Processing ». Psychological Science 9, no 2 (mars 1998) : 111–18. http://dx.doi.org/10.1111/1467-9280.00020.

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Models of human visual processing start with an initial stage with parallel independent processing of different physical attributes or features (e.g., color, orientation, motion). A second stage in these models is a temporally serial mechanism (visual attention) that combines or binds information across feature dimensions. Evidence for this serial mechanism is based on experimental results for visual search. I conducted a study of visual search accuracy that carefully controlled for low-level effects: physical similarity of target and distractor, element eccentricity, and eye movements. The larger set-size effects in visual search accuracy for briefly flashed conjunction displays, compared with feature displays, are quantitatively predicted by a simple model in which each feature dimension is processed independently with inherent neural noise and information is combined linearly across feature dimensions. The data are not predicted by a temporally serial mechanism or by a hybrid model with temporally serial and noisy processing. The results do not support the idea that a temporally serial mechanism, visual attention, binds information across feature dimensions and show that the conjunction-feature dichotomy is due to the noisy independent processing of features in the human visual system.
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Zhang, Xingxing, Chao Xu, Wanli Xue, Jing Hu, Yongchuan He et Mengxin Gao. « Emotion Recognition Based on Multichannel Physiological Signals with Comprehensive Nonlinear Processing ». Sensors 18, no 11 (11 novembre 2018) : 3886. http://dx.doi.org/10.3390/s18113886.

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Multichannel physiological datasets are usually nonlinear and separable in the field of emotion recognition. Many researchers have applied linear or partial nonlinear processing in feature reduction and classification, but these applications did not work well. Therefore, this paper proposed a comprehensive nonlinear method to solve this problem. On the one hand, as traditional feature reduction may cause the loss of significant amounts of feature information, Kernel Principal Component Analysis (KPCA) based on radial basis function (RBF) was introduced to map the data into a high-dimensional space, extract the nonlinear information of the features, and then reduce the dimension. This method can provide many features carrying information about the structure in the physiological dataset. On the other hand, considering its advantages of predictive power and feature selection from a large number of features, Gradient Boosting Decision Tree (GBDT) was used as a nonlinear ensemble classifier to improve the recognition accuracy. The comprehensive nonlinear processing method had a great performance on our physiological dataset. Classification accuracy of four emotions in 29 participants achieved 93.42%.
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Schendan, Haline E., et Marta Kutas. « Neurophysiological evidence for transfer appropriate processing of memory : Processing versus feature similarity ». Psychonomic Bulletin & ; Review 14, no 4 (août 2007) : 612–19. http://dx.doi.org/10.3758/bf03196810.

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Kainat, Jaweria, Syed Sajid Ullah, Fahd S. Alharithi, Roobaea Alroobaea, Saddam Hussain et Shah Nazir. « Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques ». Complexity 2021 (30 décembre 2021) : 1–12. http://dx.doi.org/10.1155/2021/9736179.

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Existing plant leaf disease detection approaches are based on features of extracting algorithms. These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. Diseases of a plant can be classified from their symptoms. We proposed a cucumber leaf recognition approach, consisting of five steps: preprocessing, normalization, features extraction, features fusion, and classification. Otsu’s thresholding is implemented in preprocessing and Tan–Triggs normalization is applied for normalizing the dataset. During the features extraction step, texture and shape features are extracted. In addition, increasing the instances improves some characteristics. Through a principal component analysis approach, serial feature fusion is employed to provide a feature score. Fused features can be classified through a support vector machine. The accuracy of the Fine KNN is 94.30%, which is higher than the previous work in past papers.
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Li, Zhaopeng, Deyun Zhong, Liguan Wang, Qiwang Tang et Zhaohao Wu. « Mesh Processing for Snapping Feature Points and Polylines in Orebody Modeling ». Mathematics 10, no 15 (25 juillet 2022) : 2593. http://dx.doi.org/10.3390/math10152593.

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The 3D refinement modeling of the orebody provides an important guarantee for the estimation of the resources and reserves of an ore deposit. Implicit modeling techniques can effectively improve the efficiency of orebody modeling and facilitate the dynamic updating of the model. However, due to the problems of ambiguity and missing features during implicit surface interpolation and implicit surface reconstruction, the mesh models of orebodies obtained by means of implicit modeling techniques do not easily snap to the geological feature points and feature polylines obtained based on geological sampling data. In essence, all models are inaccurate, but geological sampling data are very useful and valuable, which should be accurately and effectively involved in the orebody modeling process. This would help to improve the reliability of resource estimation and mining design. The main contribution of this paper is the proposal of a method for accurately snapping orebody features after implicit modeling. This method enables the orebody model to snap accurately to the geological feature points and feature polylines and realizes the accurate clipping of the model boundary. We tested the method with real geological datasets. The results showed that the method is applicable and effective when the geological feature points and feature polylines are close to those of the orebody mesh model and the shape trend changes little, and the model can thus meet the practical application requirements of mines.
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Du, Chen, Yanna Wang, Chunheng Wang, Cunzhao Shi et Baihua Xiao. « Selective feature connection mechanism : Concatenating multi-layer CNN features with a feature selector ». Pattern Recognition Letters 129 (janvier 2020) : 108–14. http://dx.doi.org/10.1016/j.patrec.2019.11.015.

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Krumbholz, Katrin, Simon B. Eickhoff et Gereon R. Fink. « Feature- and Object-based Attentional Modulation in the Human Auditory “Where” Pathway ». Journal of Cognitive Neuroscience 19, no 10 (octobre 2007) : 1721–33. http://dx.doi.org/10.1162/jocn.2007.19.10.1721.

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Attending to a visual stimulus feature, such as color or motion, enhances the processing of that feature in the visual cortex. Moreover, the processing of the attended object's other, unattended, features is also enhanced. Here, we used functional magnetic resonance imaging to show that attentional modulation in the auditory system may also exhibit such feature- and object-specific effects. Specifically, we found that attending to auditory motion increases activity in nonprimary motion-sensitive areas of the auditory cortical “where” pathway. Moreover, activity in these motion-sensitive areas was also increased when attention was directed to a moving rather than a stationary sound object, even when motion was not the attended feature. An analysis of effective connectivity revealed that the motion-specific attentional modulation was brought about by an increase in connectivity between the primary auditory cortex and nonprimary motion-sensitive areas, which, in turn, may have been mediated by the paracingulate cortex in the frontal lobe. The current results indicate that auditory attention can select both objects and features. The finding of feature-based attentional modulation implies that attending to one feature of a sound object does not necessarily entail an exhaustive processing of the object's unattended features.
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Akuli, Amitava, Samikshan Das, Anil Kumar Bag, Suparna Parua et Alokesh Ghosh. « Morphological Image Processing for Cocoa Bean Classification ». YMER Digital 21, no 06 (15 juin 2022) : 413–26. http://dx.doi.org/10.37896/ymer21.06/40.

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The purpose of this paper is to offer a machine vision approach for classifying cocoa beans based on their morphological properties. Using traditional machine learning approaches, the shape and size of cocoa beans were retrieved from photographs. A series of image processing techniques are used to extract the features from the photos. Finally, typical machine learning approaches such as KNN, SVM, Decision Tree, and Random Forest are used to divide the cocoa beans into four groups: large, medium, small, and rejected. A comparison of different methodologies is also carried out. Two optimization strategies, Univariate Selection and Feature Importance, are used to maximize retrieved features prior to training the model. For performance analysis, trained models are evaluated using stratified K-fold cross validations and the mean cross validation score is produced. The Random Forest Classifier has the greatest accuracy score of 0.75, according to the results of the experiments. Keywords: Cocoa beans, Classification, Image processing, Machine Learning, Feature Optimization.
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Sieler, Konstantin, Ady Naber et Werner Nahm. « An Evaluation of Image Feature Detectors Based on Spatial Density and Temporal Robustness in Microsurgical Image Processing ». Current Directions in Biomedical Engineering 5, no 1 (1 septembre 2019) : 273–76. http://dx.doi.org/10.1515/cdbme-2019-0069.

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AbstractOptical image processing is part of many applications used for brain surgeries. Microscope camera, or patient movement, like brain-movement through the pulse or a change in the liquor, can cause the image processing to fail. One option to compensate movement is feature detection and spatial allocation. This allocation is based on image features. The frame wise matched features are used to calculate the transformation matrix. The goal of this project was to evaluate different feature detectors based on spatial density and temporal robustness to reveal the most appropriate feature. The feature detectors included corner-, and blob-detectors and were applied on nine videos. These videos were taken during brain surgery with surgical microscopes and include the RGB channels. The evaluation showed that each detector detected up to 10 features for nine frames. The feature detector KAZE resulted in being the best feature detector in both density and robustness.
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